In this project we will develop and exploit new multi-sensor techniques to reduce the N20-emmissions from wheat production, by increasing the efficiency of fertilizer-, weed-, and disease control. To do so we will follow the principle of precision agriculture, i.e. the application of inputs according to the spatial variability within a field.
Starting with fertilization, we intend to improve an existing spectral system for site-specific application, which is unable to distinguish between the spectral characteristics of plants deficient in N and those suffering to water stress.
The effects of site-specific fertilizer management on the N2O-emissions will also be documented.
To increase the currently low efficiency of N2O-flux measurements, we will construct a programmable, unmanned ground vehicle equipped with a gas monitoring system. This will greatly enhance our ability to quantify annual N2O emissions, as affected by treatments.
The work on weeds comprises construction and calibration of image analysis for perennial weed species detection, and translation of sensed data into site-specific control actions. Discriminative power of the image analysis under varying field conditions is the most critical factor.
Increased efficiency in disease control will be obtained by pre-symptomatic disease detection, based on induced chlorophyll fluorescence and volatile organic compounds (VOC) profile determination. One critical aspect here will be whether recent developments of e.g. electronic noses will make the detection of fungal VOC profiles possible in the field.
Finally, we intend to combine our human and technological resources and perform comprehensive integrated analyses to unravel significant interactive effects on plant performance which should be accounted for in an optimized system designed to reduce the environmental footprint of food production. Both, public and private industry sectors as well as the civil society at large may benefit from the project results.
Primary objective of the project is to:
develop and exploit new multi-sensor techniques for optimizing fertilization, weed-, and disease control to improve yields and reduce the N20-emmissions related to wheat production.
The secondary objectives are realized within specific work packages (WP’s):
4. Develop techniques for pre-symptomatic disease detection suitable for site-specific fungicide application systems (WP 4)
5. Gain knowledge on how interactions between N-requirement, weed infestation and diseases may influence a site-specific management of fertilizer, herbicides and fungicides (WP 5)